In 1958 Robert Guthrie developed a simple blood test to screen newborns for Phenylketonuria (PKU). Since then, newborn screening has become one of the nations most successful public health programs. To be included on the newborn screening panel, a disorder must be identifiable at birth and individuals with the disorder must benefit from early identification and intervention. Such interventions should be effective and come at a reasonable cost.
Newborn screening is currently recommended for 29 core disorders and 25 secondary disorders. All states have implemented this core panel, resulting in 12,500 babies diagnosed with a newborn screening disorder each year. Innovations in analytical technology over the past decade have enabled expansion of the number of conditions included in newborn screening and facilitated research on a growing number of candidate conditions which meet the selection criteria.
Past research has already shown the benefits of newborn screening and subsequent early intervention and treatment. In the case of PKU, untreated individuals suffer enormous burdens. While persons with PKU will have a relatively normal lifespan, those left untreated will experience profound intellectual disability with an IQ frequently below 20. On the other hand, persons identified and treated from birth will have a normal IQ. Similar experiences are seen in other newborn screen disorders. Persons with Medium Chain Acyl-CoA Dehydrogenase Deficiency (MCADD) are at substantial risk for sudden death. Persons with Severe Combined Immunodeficiency (SCID) will die from infections within the first year if not identified and treated.
In 2008, the National Institute of Child Health & Human Development (NICHD) established the Newborn Screening Translational Research Network (NBSTRN) coordinating center to develop and maintain tools to support continued research in newborn screening. Screening and short-term follow-up (STFU) are well established nationally, yet there is no national system to collect and analyze longitudinal health information. Long-term follow-up (LTFU) is key to understanding the impact of newborn screening and contributes to the understanding of the natural history of the condition. Additionally, it enables identification of inequities in health care delivery and facilitates quality improvement.
Though a subcontract to the American College of Medical Genetics (ACMG), CBMi was tasked with developing a long-term follow-up (LTFU) system in order to enable data collection, analysis, and data mining across the lifespan by connecting research and clinical activities and data. The resource itself consists of data elements and definitions within a data capture tool and management system. The LTFU project leverages REDCap (Research Electronic Data Capture) to complete these tasks.
REDCap is an application that allows users to build and manage web-based surveys and databases quickly and securely. REDCap was chosen as the data collection and management system for the LTFU project for a variety of reasons:
- REDCap data entry forms are web-based and easy to use. For the end user, there is no software to install or complicated program to learn.
- Multi-site access and permissioning allows researchers at multiple sites and institutions to access the same project while only seeing their own data, allowing for large-scale data collection while maintaining patient privacy.
- The dynamic nature of REDCap allows for changes to be made immediately both before and during the study.
- Auto-validation allows for real-time data monitoring and feedback for data entry persons.
- Complex branching logic allows for single forms to be customized to individual conditions and situations.
- Data can be analyzed immediately using the ‘Graphical Data View & Stats’ application or can be exported as a raw data file for Excel, SAS, Stata, R, or SPSS.
- Exports of the data dictionary can be expanded in other applications to allow collaboration and standardization.
The simplicity and elegant design of REDCap combined with the building of advanced functionality will allow the LTFU project to amass a large amount of data across a multitude of clinical sites. Compilation of longitudinal datasets in rare diseases will allow for better research, better treatments, and improved outcomes.